Artificial Intelligence-Assisted Lesion-Based Urgent Referral Triage of Ultra-Widefield Retinal Images: A Multi-Reader Multi-Case Randomized Reader Study (ALERT-UWF)
Clinical Utility of an Artificial Intelligence-Assisted Lesion-Based Urgent Referral Triage System for Ultra-Widefield Retinal Images: A Prospective Multi-Reader Multi-Case Randomized Reader Study
his study evaluates the clinical utility of an artificial intelligence (AI)-assisted lesion-based urgent referral triage system for ultra-widefield (UWF) retinal images.
Unlike disease-classification systems, the AI system identifies predefined vision-threatening retinal findings and generates lesion-level urgent referral recommendations. Participating ophthalmologists will evaluate UWF retinal images under randomized AI-assisted and unassisted conditions.
The primary objective is to determine whether lesion-based AI assistance improves urgent referral triage performance compared with unaided image interpretation.
Studieoversikt
Status
Forhold
Intervensjon / Behandling
Detaljert beskrivelse
Ultra-widefield retinal imaging is increasingly used for retinal disease screening and referral triage. Many vision-threatening retinal abnormalities require timely identification and referral to retinal specialists.
The AI system evaluated in this study is designed as a lesion-based triage tool rather than a disease-diagnosis system. The model identifies predefined urgent referral retinal findings and generates referral recommendations based on lesion-level evidence.
Urgent referral findings include:
- Retinal detachment
- Untreated retinal tear or retinal hole
- Vitreous hemorrhage
- Pre-retinal hemorrhage
- Subretinal hemorrhage
- Retinal neovascularization
- Optic disc neovascularization
- Tractional fibrovascular membrane Treated retinal tears associated with laser barricade scars are classified as non-urgent referral findings.
A total of 600 UWF retinal images acquired using Zeiss and Optos imaging systems will be included.
Participating ophthalmologists will independently evaluate images in randomized AI-assisted and unassisted settings.
The primary objective is to determine whether AI assistance improves lesion-based urgent referral triage accuracy.
Studietype
Registrering (Antatt)
Fase
- Ikke aktuelt
Kontakter og plasseringer
Studiekontakt
- Navn: Xiuju Chen, md
- Telefonnummer: +8618060955810
- E-post: joyychen@aliyun.com
Deltakelseskriterier
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
- Voksen
- Eldre voksen
Tar imot friske frivillige
Beskrivelse
Inclusion Criteria:
- Licensed ophthalmologists
- Willing to participate as readers
- Completion of study training
Exclusion Criteria:
- Retinal specialists involved in establishing gold-standard labels
- Prior access to gold-standard labels
- Incomplete study participation
Studieplan
Hvordan er studiet utformet?
Designdetaljer
- Primært formål: Diagnostisk
- Tildeling: Randomisert
- Intervensjonsmodell: Faktoriell oppgave
- Masking: Ingen (Open Label)
Våpen og intervensjoner
Deltakergruppe / Arm |
Intervensjon / Behandling |
|---|---|
|
Eksperimentell: AI-Assisted Interpretation
Readers interpret UWF retinal images with lesion-level AI findings and urgent referral recommendations.
|
Readers interpret UWF retinal images with lesion-level AI findings and urgent referral recommendations.
|
|
Aktiv komparator: Unassisted Interpretation
Readers interpret UWF retinal images without AI assistance.
|
Readers interpret UWF retinal images without AI assistance.
|
Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
Correct Lesion-Based Urgent Referral Triage Rate
Tidsramme: Immediately after image interpretation.
|
Proportion of reader referral decisions consistent with expert-adjudicated lesion-based urgent referral classifications.
|
Immediately after image interpretation.
|
Sekundære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
Sensitivity for Urgent Referral Findings
Tidsramme: Immediately after image interpretation.
|
Sensitivity for Urgent Referral Findings
|
Immediately after image interpretation.
|
|
Specificity for Urgent Referral Findings
Tidsramme: Immediately after image interpretation.
|
Specificity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels.
|
Immediately after image interpretation.
|
|
False-Negative Rate for Urgent Referral Findings
Tidsramme: Immediately after image interpretation.
|
Proportion of urgent referral images incorrectly classified as non-urgent referral by readers.
|
Immediately after image interpretation.
|
|
False-Positive Rate for Urgent Referral Findings
Tidsramme: Immediately after image interpretation.
|
Proportion of non-urgent referral images incorrectly classified as urgent referral by readers.
|
Immediately after image interpretation.
|
|
Reader Confidence Score
Tidsramme: Immediately after image interpretation.
|
Reader-reported confidence level for referral decisions measured using a 5-point Likert scale, ranging from 1 (very uncertain) to 5 (very confident).
|
Immediately after image interpretation.
|
|
Change in Correct Urgent Referral Decisions After AI Assistance
Tidsramme: Immediately after image interpretation.
|
Number and proportion of cases in which AI assistance changed an incorrect referral decision to a correct referral decision.
|
Immediately after image interpretation.
|
Samarbeidspartnere og etterforskere
Studierekorddatoer
Studer hoveddatoer
Studiestart (Antatt)
Primær fullføring (Antatt)
Studiet fullført (Antatt)
Datoer for studieregistrering
Først innsendt
Først innsendt som oppfylte QC-kriteriene
Først lagt ut (Faktiske)
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
Siste oppdatering sendt inn som oppfylte QC-kriteriene
Sist bekreftet
Mer informasjon
Begreper knyttet til denne studien
Nøkkelord
Ytterligere relevante MeSH-vilkår
Andre studie-ID-numre
- XMYKZX-KY-2026-011
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